Ai, Statistics & Data Science in Practice Webinar - April 17, 2026

Tuesday, April 17, 2026 - 12:00pm to 1:30pm ET

Overview

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Speaker

Anastasios N Angelopoulos, Cofounder and CEO at LMArena AI

Moderator

Will Wei Sun, Associate Professor of Quantitative Methods, Daniels School of Business, Purdue University

About the Speaker

Anastasios Nikolas Angelopoulos is a cofounder and the CEO at LMArena AI. He received his Ph.D. at the University of California, Berkeley, advised by Michael I. Jordan and Jitendra Malik. From 2016 to 2019, he was an electrical engineering student at Stanford University advised by Gordon Wetzstein and Stephen P. Boyd. His research interests include Theoretical statistics + black-box machine learning models. How can we do statistics with ML in the loop, and no assumptions on the model at all? I'm especially interested in simple, easy-to-use algorithms with formal mathematical validity guarantees. Much of my work has centered around conformal prediction and prediction-powered inference. He also has a research interest in evaluations, and seeks to measure how we effectively test and evaluate models to make sure they are safe and performant. He helped build the leading online platform for evaluating Large Language Models, Chatbot Arena. He has also thought about methods for autoevaluation: using synthetic data for efficient and cost-effective evaluation. His other research interests include computer vision, computational imaging, biomedicine. His research career began in imaging and clinical medicine, and these are still his greatest motivations. He seeks to build computational and statistical systems that change the way we understand the human body and treat diseases. End-to-end approaches involving automatic vision-based reconstruction, recognition, and decision-making often excite me most. At Berkeley, he was the recipient of the Leon O. Chua Department Award. He was supported by a National Science Foundation Graduate Research Fellowship and a Berkeley Fellowship. He is also a Sequoia Open Source Software Fellow. At Stanford, he was a National Merit Scholar and received the Terman Award, Phi Beta Kappa, Tau Beta Pi, and departmental distinction. See Profile

 

About the Moderator

Dr. Will Wei Sun is an Associate Professor of Quantitative Methods at Purdue University's Mitchell E. Daniels, Jr. School of Business, with a courtesy appointment in the Department of Statistics. He serves as the PhD Coordinator for Quantitative Methods and is recognized for his expertise in statistical foundations of large language models, trustworthy reinforcement learning, tensor data analysis. Dr. Sun's research has been supported by notable grants from the National Science Foundation and the Office of Naval Research. Dr. Sun earned his Ph.D. in Statistics from Purdue University in 2015. Before that, he was a research scientist at Yahoo Labs and an assistant professor at Miami Business School. Dr. Sun is on the editorial board for Annals of Applied Statistics, Statistical Analysis and Data Mining. See Profile

 

About AI, StAtIstics and Data Science in Practice

The NISS AI, Statistics and Data Science in Practice is a monthly event series will bring together leading experts from industry and academia to discuss the latest advances and practical applications in AI, data science, and statistics. Each session will feature a keynote presentation on cutting-edge topics, where attendees can engage with speakers on the challenges and opportunities in applying these technologies in real-world scenarios. This series is intended for professionals, researchers, and students interested in the intersection of AI, data science, and statistics, offering insights into how these fields are shaping various industries. The series is designed to provide participants with exposure to and understanding of how modern data analytic methods are being applied in real-world scenarios across various industries, offering both theoretical insights, practical examples, and discussion of issues.

During Spring 2026, from January through May 2026, the series will focus on large language models (LLMs) and the statistical and methodological foundations required to develop, evaluate, and deploy them responsibly and effectively. As LLMs become central to a wide range of scientific, industrial, and societal applications, careful attention to data generation, model training, evaluation, and inference is essential to ensure reliability, robustness, and transparency. As LLMs become increasingly central to scientific research, industry workflows, and societal decision-making, rigorous attention to how training data are constructed, curated, and sampled is critical for understanding model behavior and limitations. The series will highlight methodological considerations in model training and fine-tuning, including sources of bias, variability, and uncertainty, as well as principled approaches to benchmarking and evaluation that move beyond surface-level performance metrics. Emphasis will be placed on transparent and reproducible evaluation frameworks that support meaningful comparisons across models and use cases, and on statistical perspectives that help clarify what LLM outputs do and do not represent. By grounding discussions of LLM development and deployment in sound statistical reasoning, the series aims to promote more reliable, interpretable, and trustworthy language models in practice.

See full list of featured topics (also below)

Featured Topics:

Event Type

  • NISS Hosted

Cost

Free Webinar

Website

Register on Zoom here!

Location

Free Zoom Webinar
United States

Policy